The technology of the future is developing much further than we can imagine. The number of connected devices (we’re talking about IoT) in the world is growing, and companies have to think about how to improve the data quality of connected devices. What if cloud or enterprise data center options are not the only solutions? Currently, most of the new data capabilities lie at the “edge.”
The ability of edge computing to bring devices closer to the data source, minimize response times and eliminate latency is attractive to companies. However, these are not all the benefits that edge technology can boast. The emergence of edge computing indicates that there is little space left for growth in the cloud. Below are the characteristics of edge computing and prominent examples of the use of this technology.
Defining edge computing
Edge computing is the processing of data at the edge of the network (near the data source or the place of creation), instead of centrally in a cloud computing center or private data center. Speaking of the network edge, this is where a device or local network connects to the Internet. The edge is near the devices with which it communicates and is the point of entry to the network. To summarize, the 3 main elements of edge computing are:
- Device edge: where the edge devices reside;
- Local edge: includes the infrastructure to support apps and network workloads;
- Cloud: the node of the environment where all the data comes together.
Examples of edge computing include wearable devices, computers that analyze traffic flow, the Internet of Things, streaming video optimization, and various urban and global network access devices.
Due to processing and storing data at the “edge”, edge computing provides reliable, real-time data, reduces costs, improves response times and speeds up operations. In addition, edge computing helps improve cybersecurity by reducing the need to interact with cloud platforms and networks. If these benefits don’t seem enough to you, let’s compare edge computing with other computing.
Edge vs cloud computing
Edge computing and cloud computing have many differences. If cloud computing is about running workloads in the cloud, then edge computing is about running workloads on edge devices. So, here are the main differences between edge and cloud computing:
|Processing data close to the user (with and without Internet access) makes edge computing more reliable, as the risk of security threats and network failures is reduced
|Access to the internet is required at all times to run in the cloud and software bugs can lead to poor performance
|Immediate, real-time data processing
|Maximum data processing speed in the cloud or on a vendor’s server
|Require more bandwidth, reduce the burden of data traffic, reduce latency and provide reliable performance
|Require rapid resource changes to meet computational needs, optimal cost of cloud services, provide reliability, and scalability
|High, because the data are stored at the edge, where confidentiality is very sensitive to security threats
|High, because of the implementation of advanced cybersecurity measures by vendors and organizations
|Reliable infrastructure is needed to scale the edge network on time
|Scaling is fast and easy with no downtime or failures.
The biggest difference between the two technologies is where the data is processed. Cloud computing is centralized, which means that IoT data processing takes place in the cloud, on a centralized network of servers. While edge computing is decentralized, which means that data processing is moved from centralized servers to the edge of the network or cloud, which is located near the data source.
The question may arise, which computing is better for companies to use? If companies are working with big data that is not time-sensitive, it is better to use cloud platforms. However, when it comes to multiple devices, where the focus is not on volume but real-time data processing, then using edge devices/services may be a better solution.
The problems that edge computing solves
The desire to consume data without any delays pushes companies to bring data centers closer to customers, thereby triggering the need for edge computing. Below are a few problems that edge computing can solve.
IoT devices must be connected to the cloud to store and process data. Consequently, transferring IoT data to the cloud requires a lot of power and high bandwidth. Networks have limited bandwidth, there is a finite limit to the amount of data or the number of devices that can transmit data over the network. Edge computing in IoT helps companies reduce Internet bandwidth usage and reduce bandwidth costs because large amounts of data are processed locally, close to the source.
A device connected to the Internet must respond quickly to compensate for the latency inherent in transmitting data over a distance. The greater the distance between where the data is created and where it is processed, the lower the processing speed, or latency, is. Edge computing can eliminate the latency problem because it ensures that there is no discontinuity in real-time processing and thus creates a more reliable network.
Edge devices are still vulnerable to hacking. Decentralizing edge computing eliminates many of the drawbacks associated with centralized data centers. Edge computing providers can develop a multi-layered security strategy. And given that edge computing can process data across multiple nodes and even devices, it enhances and strengthens data security and privacy.
As data grows, the cost of moving that data increases. And to handle large data loads, high bandwidth is required, further increasing network costs. Edge computing eliminates the need to move data from endpoint devices to the cloud and back again. Reducing data movement reduces process time or latency, resulting in lower costs. Thus, edge computing can help companies contain costs, or at least keep them from rising, by reducing the amount of data being moved to and from the cloud.
Edge Computing can process data locally, without the need for constant access to the Internet. Edge computing can move necessary information from edge computing back to the data center when the connection is available. In this way, edge computing improves fault tolerance, in other words, the failure of one edge device will not affect the performance of other edge devices, which increases the reliability of the connected environment.
Cases of using edge computing
Examples of edge computing can be found in various fields and industries. Let’s shed some light on the most prominent uses of edge computing.
Smart homes rely on IoT devices that collect and process data from the home. This data is then sent to a centralized remote server where it is processed and stored, often leading to security risks and delays. With edge computing, data travel times can be shortened and sensitive information is processed only at the edge.
Autonomous vehicles using edge computing analyze real-time data needed for fast, reliable driving. With edge computing, the need for a driver in each truck is eliminated, except for the first one, because the trucks will be able to communicate with each other with ultra-low latency. Thus, a group of trucks moving closely behind each other can save fuel, reduce congestion, and lead to safe transportation without manual intervention.
Sensors and IoT devices connected to the edge platform in factories, plants, and offices are used to monitor energy use and analyze energy consumption in real-time. Through edge computing and broader adoption of smart grids, businesses can better manage their energy consumption.
Edge computing provides efficient urban traffic management, reducing traffic capacity costs and delays. Examples include optimizing bus frequency for demand fluctuations or managing the opening and closing of additional lanes.
Edge computing in the form of a hospital website can process data locally to ensure confidentiality of that data. Edge also enables timely notification of unusual patient trends or behaviors to physicians (via analytics/AI) and the creation of patient monitoring dashboards.
Industrial companies can use edge computing to monitor production. Real-time analytics and Machine Learning at the edge, allow companies to find production errors and improve product quality. Edge computing helps spread sensors throughout the manufacturing plant, providing data on how each component of a product is assembled and stored, as well as how long components stay in stock.
Edge computing is not buzzword, but rather an opportunity to go beyond the limitations of traditional cloud networks. However, with edge computing, companies can get even closer to the consumer, redefine products and services, and improve business efficiency. It is unlikely that users accustomed to using a Fitbit to measure physical fitness or a Nest Thermostat to monitor the temperature in the house will abandon them. In contrast, interest and demand for edge computing services and products will grow. The question is how fast.
Equipped with a Bachelor of Information Technology (BIT) degree, Lucas Noah stands out in the digital content creation landscape. His current roles at Creative Outrank LLC and Oceana Express LLC showcase his ability to turn complex technology topics into engagin... Read more